Managing Microplastics in Saint John, New Brunswick: A Grassroots Action Utilizing the One Health Framework
Bibliographic record
Abstract
Plastic pollution in marine environments has become a global crisis, with microplastics posing significant threats to the health of all one health model stakeholders: humans, non-human animals, and ecosystems. The persistent and pervasive nature of plastics makes ocean plastic pollution a complex and interconnected “wicked problem.” This paper explores the LINT LUV-R initiative by the Atlantic Coastal Action Program in Saint John, a grassroots, community-based approach to preventing microplastic and microfiber accumulation in Saint John Harbour by installing microfiber filters in washing machines. Unique in its preventive methodology, this initiative captures microfibers before they enter aquatic ecosystems, addressing a critical source of microplastic contamination in this region. Grounded in the principles of One Health, this initiative recognizes the interdependence of human, non-human animal, and environmental health. This inclusive approach fosters collaboration among diverse stakeholders, including Indigenous communities, fishers, environmental nonprofits, government agencies, and the community, to promote sustainable solutions for Saint John Harbour. The initiative demonstrates measurable success, capturing millions of microfibers annually while empowering participants through education and citizen science. By combining preventive action, cultural sensitivity, and stakeholder engagement, the LINT LUV-R initiative offers a replicable model for combating microplastic pollution in other coastal regions. This paper highlights the necessity of community-led, multidisciplinary approaches to solve wicked environmental problems and advance the health of interconnected systems, prioritizing the health of non-human animals, humans, and ecosystems equally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".